Gait Correlation Analysis Based Human Identification

Human gait identification aims to identify people by a sequence of walking images. Comparing with fingerprint or iris based identification, the most important advantage of gait identification is that it can be done at a distance. In this paper, silhouette correlation analysis based human identification approach is proposed. By background subtracting algorithm, the moving silhouette figure can be extracted from the walking images sequence. Every pixel in the silhouette has three dimensions: horizontal axis (x), vertical axis (y), and temporal axis (t). By moving every pixel in the silhouette image along these three dimensions, we can get a new silhouette. The correlation result between the original silhouette and the new one can be used as the raw feature of human gait. Discrete Fourier transform is used to extract features from this correlation result. Then, these features are normalized to minimize the affection of noise. Primary component analysis method is used to reduce the features' dimensions. Experiment based on CASIA database shows that this method has an encouraging recognition performance.

[1]  Chung-Lin Huang,et al.  Gait Analysis For Human Identification Through Manifold Learning and HMM , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[2]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[3]  Euntai Kim,et al.  Probabilistic gait modelling and recognition , 2013, IET Comput. Vis..

[4]  Amit K. Roy-Chowdhury,et al.  GAIT-BASED HUMAN IDENTIFICATION FROM A MONOCULAR VIDEO SEQUENCE , 2003 .

[5]  Yohan Dupuis,et al.  Feature subset selection applied to model-free gait recognition , 2013, Image Vis. Comput..

[6]  Takio Kurita,et al.  A New Scheme for Practical Flexible and Intelligent Vision Systems , 1988, MVA.

[7]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Haiying Shen,et al.  Leveraging Social Networks to Combat Collusion in Reputation Systems for Peer-to-Peer Networks , 2013, 2011 IEEE International Parallel & Distributed Processing Symposium.

[9]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  T D Albright,et al.  Visual motion perception. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[11]  S. Stevenage,et al.  Visual analysis of gait as a cue to identity , 1999 .

[12]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Reza Safabakhsh,et al.  Model-based human gait recognition using leg and arm movements , 2010, Eng. Appl. Artif. Intell..

[14]  Mark S. Nixon,et al.  Gait Recognition By Walking and Running: A Model-Based Approach , 2002 .

[15]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[16]  Tieniu Tan,et al.  Fusion of static and dynamic body biometrics for gait recognition , 2003, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  P. A. Hageman,et al.  Comparison of gait of young men and elderly men. , 1986, Physical therapy.

[18]  Mark S. Nixon,et al.  Automatic extraction and description of human gait models for recognition purposes , 2003, Comput. Vis. Image Underst..

[19]  Haihong Hu,et al.  Frame difference energy image for gait recognition with incomplete silhouettes , 2009, Pattern Recognit. Lett..

[20]  Yilong Yin,et al.  Gait Recognition Based on Outermost Contour , 2011, Int. J. Comput. Intell. Syst..

[21]  Haihong Hu,et al.  Factorial HMM and Parallel HMM for Gait Recognition , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Mark S. Nixon,et al.  Automated person recognition by walking and running via model-based approaches , 2004, Pattern Recognit..

[23]  Mark S. Nixon,et al.  Gender Classification in Human Gait Using Support Vector Machine , 2005, ACIVS.

[24]  Haifeng Hu,et al.  Enhanced Gabor Feature Based Classification Using a Regularized Locally Tensor Discriminant Model for Multiview Gait Recognition , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Jianhua Ma,et al.  Behavior-based reputation management in P2P file-sharing networks , 2012, J. Comput. Syst. Sci..

[26]  Michael W. Whittle,et al.  Clinical gait analysis: A review , 1996 .

[27]  Tieniu Tan,et al.  A Study on Gait-Based Gender Classification , 2009, IEEE Transactions on Image Processing.